Speaker
Description
Handling infrared cameras is often challenging due to the diversity of manufacturers and communication interfaces. Although camera communication may rely on generic standards such as GenICam, it frequently depends on vendor-specific Software Development Kits (SDKs) and proprietary software. As a result, integrating custom algorithms and building reproducible processing pipelines across different camera models becomes difficult. Switching between proprietary tools is inconvenient and can be particularly cumbersome when managing multiple cameras simultaneously, especially in laboratory environments.
To address these challenges, a modern Python-based framework IRLab for infrared camera handling has been developed. It provides a generic Application Programming Interface (API) that allows interaction with most infrared cameras available on the market, using either standard protocols or vendor-specific SDKs when required. The primary advantage of this framework is the provision of a unified interface that abstracts away vendor-specific implementations, enabling code reuse across camera models and simplifying the integration of new devices into existing codebases.
Thanks to its open-source nature and modular design, the framework facilitates the rapid integration of new camera models. Once core camera functionalities are implemented, the API automatically exposes parameter configuration, recording management, and live streaming capabilities via WebRTC or RTSP for instance.
Building on this API, a web-based interface has been developed to automatically scan for and discover supported cameras connected to the system. This interface provides a user-friendly environment for planning image or video recordings and interacting with multiple cameras through a single, unified control panel. When deployed in server mode, it also enables remote camera monitoring and control.
Moreover, a generic and sustainable approach must also account for the entire data life-cycle: how data are acquired, stored, accessed, processed, and archived. This consideration is especially critical for field instrumentation, where reliability, scalability, and long-term data management are essential. Current developments [1] enable such possibilities to make dataset management efficient and secure for collaborative research projects, while considering diverse data types, sharing requirements, and compliance regulations.
In this work, we first introduce the newly developed IRLab generic framework. We then describe its integration with previous developments for large-scale dataset management. Finally, a concrete use case is presented to illustrate the capabilities of the proposed approach, before outlining directions for future developments.
Acknowledgement
BRIGHTER has received funding from the Chips Joint Undertaking (JU) under grant agreement No 101096985. The JU receives support from the European Union’s Horizon Europe research and innovation program and France, Belgium, Portugal, Spain, Turkey.
Bibliography
- J. Dumoulin, T. Toullier, N. Gey, and M. Malandain, “DAM2 -Data, Model and Monitoring A Scalable and Compliant Solution for Managing enriched Infrared images as FAIR Research Data” Apr. 2025. doi: 10.5281/zenodo.15182568.
